To what extent does the volume of target-language unlabeled data in self-supervised pre-training correlate wit

Abstract

Abstract: Automatic speech recognition for low-resource languages remains fundamentally constrained by the scarcity of labeled data and computational resources required by state-of-the-art models. We present a systematic investigation into cross-lingual continuous pretraining for low-resource languages, using Perso-Arabic languages (Persian, Arabic, and Urdu) as our primary case study. Our approach demonstrates that strategic utilization of unlabeled speech data can effectively bridge the resource gap without sacrificing recognition accuracy. We construct a 3,000-hour multilingual corpus through a scala

Research Question

To what extent does the volume of target-language unlabeled data in self-supervised pre-training correlate with downstream ASR accuracy on low-resource dialects?

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